OpenRaiser/Intern-Atlas huggingface dataset:
Data-driven Approach to Measuring the Level of Press Freedom Using Media Attention Diversity from Unfiltered News
Venue: arXiv cs.CY 2017
Year: 2017

Published by Reporters Without Borders every year, the Press Freedom Index (PFI) reflects the fear and tension in the newsroom pushed by the government and private sectors. While the PFI is invaluable in monitoring media environments worldwide, the current survey-based method has inherent limitations to updates in terms of cost and time. In this work, we introduce an alternative way to measure the level of press freedom using media attention diversity compiled from Unfiltered News.
The UMCD Dataset
Venue: arXiv cs.CV 2017
Year: 2017

In recent years, the technological improvements of low-cost small-scale Unmanned Aerial Vehicles (UAVs) are promoting an ever-increasing use of them in different tasks. In particular, the use of small-scale UAVs is useful in all these low-altitude tasks in which common UAVs cannot be adopted, such as recurrent comprehensive view of wide environments, frequent monitoring of military areas, real-time classification of static and moving entities (e.g., people, cars, etc.). These tasks can be supported by mosaicking and change detection algorithms achieved at low-altitude. Currently, public datasets for testing these algorithms are not available. This paper presents the UMCD dataset, the first collection of geo-referenced video sequences acquired at low-altitude for mosaicking and change detection purposes. Five reference scenarios are also reported.
Learning to Generate Reviews and Discovering Sentiment
Venue: arXiv cs.LG 2017
Year: 2017

We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts. Specifically, we find a single unit which performs sentiment analysis. These representations, learned in an unsupervised manner, achieve state of the art on the binary subset of the Stanford Sentiment Treebank. They are also very data efficient. When using only a handful of labeled examples, our approach matches the performance of strong baselines trained on full datasets. We also demonstrate the sentiment unit has a direct influence on the generative process of the model. Simply fixing its value to be positive or negative generates samples with the corresponding positive or negative sentiment.
CHAOS: an SDN-based Moving Target Defense System
Venue: arXiv cs.NI 2017
Year: 2017

The static nature of current cyber systems has made them easy to be attacked and compromised. By constantly changing a system, Moving Target Defense (MTD) has provided a promising way to reduce or move the attack surface that is available for exploitation by an adversary. However, the current network- based MTD obfuscates networks indiscriminately that makes some networks key services, such as web and DNS services, unavailable, because many information of these services has to be opened to the outside and remain real without compromising their usability. Moreover, the indiscriminate obfuscation also severely reduces the performance of networks. In this paper, we propose CHAOS, an SDN (Software-defined networking)-based MTD system, which discriminately obfuscates hosts with different security levels in a network. In CHAOS, we introduce a Chaos Tower Obfuscation (CTO) method, which uses a Chaos Tower Structure (CTS) to depict the hierarchy of all the hosts in an intranet and provides a more unpredictable and flexible obfuscation method. We also present the design of CHAOS, which leverages SDN features to obfuscate the attack surface including IP obfuscation, ports obfuscation, and fingerprint obfuscation thereby enhancing the unpredictability of the networking environment. We develop fast CTO algorithms to achieve a different degree of obfuscation for the hosts in each layer. Our experimental results show that a network protected by CHAOS is capable of decreasing the percentage of information disclosure effectively to guarantee the normal flow of traffic.
Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution
Venue: arXiv cs.PL 2017
Year: 2017

While modern parallel computing systems provide high performance resources, utilizing them to the highest extent requires advanced programming expertise. Programming for parallel computing systems is much more difficult than programming for sequential systems. OpenMP is an extension of C++ programming language that enables to express parallelism using compiler directives. While OpenMP alleviates parallel programming by reducing the lines of code that the programmer needs to write, deciding how and when to use these compiler directives is up to the programmer. Novice programmers may make mistakes that may lead to performance degradation or unexpected program behavior. Cognitive computing has shown impressive results in various domains, such as health or marketing. In this paper, we describe the use of IBM Watson cognitive system for education of novice parallel programmers. Using the dialogue service of the IBM Watson we have developed a solution that assists the programmer in avoiding common OpenMP mistakes. To evaluate our approach we have conducted a survey with a number of novice parallel programmers at the Linnaeus University, and obtained encouraging results with respect to usefulness of our approach.
Stability and Frequency Regulation of Inverters with Capacitive Inertia
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we address the problem of stability and frequency regulation of a recently proposed inverter. In this type of inverter, the DC-side capacitor emulates the inertia of a synchronous generator. First, we remodel the dynamics from the electrical power perspective. Second, using this model, we show that the system is stable if connected to a constant power load, and the frequency can be regulated by a suitable choice of the controller. Next, and as the main focus of this paper, we analyze the stability of a network of these inverters, and show that frequency regulation can be achieved by using an appropriate controller design. Finally, a numerical example is provided which illustrates the effectiveness of the method.
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
Venue: arXiv cs.LG 2017
Year: 2017

Deep convolutional networks have witnessed unprecedented success in various machine learning applications. Formal understanding on what makes these networks so successful is gradually unfolding, but for the most part there are still significant mysteries to unravel. The inductive bias, which reflects prior knowledge embedded in the network architecture, is one of them. In this work, we establish a fundamental connection between the fields of quantum physics and deep learning. We use this connection for asserting novel theoretical observations regarding the role that the number of channels in each layer of the convolutional network fulfills in the overall inductive bias. Specifically, we show an equivalence between the function realized by a deep convolutional arithmetic circuit (ConvAC) and a quantum many-body wave function, which relies on their common underlying tensorial structure. This facilitates the use of quantum entanglement measures as well-defined quantifiers of a deep network's expressive ability to model intricate correlation structures of its inputs. Most importantly, the construction of a deep ConvAC in terms of a Tensor Network is made available. This description enables us to carry a graph-theoretic analysis of a convolutional network, with which we demonstrate a direct control over the inductive bias of the deep network via its channel numbers, that are related to the min-cut in the underlying graph. This result is relevant to any practitioner designing a network for a specific task. We theoretically analyze ConvACs, and empirically validate our findings on more common ConvNets which involve ReLU activations and max pooling. Beyond the results described above, the description of a deep convolutional network in well-defined graph-theoretic tools and the formal connection to quantum entanglement, are two interdisciplinary bridges that are brought forth by this work.
Preliminary Experiments using Subjective Logic for the Polyrepresentation of Information Needs
Venue: arXiv cs.IR 2017
Year: 2017

According to the principle of polyrepresentation, retrieval accuracy may improve through the combination of multiple and diverse information object representations about e.g. the context of the user, the information sought, or the retrieval system. Recently, the principle of polyrepresentation was mathematically expressed using subjective logic, where the potential suitability of each representation for improving retrieval performance was formalised through degrees of belief and uncertainty. No experimental evidence or practical application has so far validated this model. We extend the work of Lioma et al. (2010), by providing a practical application and analysis of the model. We show how to map the abstract notions of belief and uncertainty to real-life evidence drawn from a retrieval dataset. We also show how to estimate two different types of polyrepresentation assuming either (a) independence or (b) dependence between the information objects that are combined. We focus on the polyrepresentation of different types of context relating to user information needs (i.e. work task, user background knowledge, ideal answer) and show that the subjective logic model can predict their optimal combination prior and independently to the retrieval process.
A Subjective Logic Formalisation of the Principle of Polyrepresentation for Information Needs
Venue: arXiv cs.IR 2017
Year: 2017

Interactive Information Retrieval refers to the branch of Information Retrieval that considers the retrieval process with respect to a wide range of contexts, which may affect the user's information seeking experience. The identification and representation of such contexts has been the object of the principle of Polyrepresentation, a theoretical framework for reasoning about different representations arising from interactive information retrieval in a given context. Although the principle of Polyrepresentation has received attention from many researchers, not much empirical work has been done based on it. One reason may be that it has not yet been formalised mathematically. In this paper we propose an up-to-date and exible mathematical formalisation of the principle of Polyrepresentation for information needs. Specifically, we apply Subjective Logic to model different representations of information needs as beliefs marked by degrees of uncertainty. We combine such beliefs using different logical operators, and we discuss these combinations with respect to different retrieval scenarios and situations. A formal model is introduced and discussed, with illustrative applications to the modelling of information needs.
A lower bound on the 2-adic complexity of modified Jacobi sequence
Venue: arXiv cs.IT 2017
Year: 2017

Let $p,q$ be distinct primes satisfying $\mathrm{gcd}(p-1,q-1)=d$ and let $D_i$, $i=0,1,\cdots,d-1$, be Whiteman's generalized cyclotomic classes with $Z_{pq}^{\ast}=\cup_{i=0}^{d-1}D_i$. In this paper, we give the values of Gauss periods based on the generalized cyclotomic sets $D_0^{\ast}=\sum_{i=0}^{\frac{d}{2}-1}D_{2i}$ and $D_1^{\ast}=\sum_{i=0}^{\frac{d}{2}-1}D_{2i+1}$. As an application, we determine a lower bound on the 2-adic complexity of modified Jacobi sequence. Our result shows that the 2-adic complexity of modified Jacobi sequence is at least $pq-p-q-1$ with period $N=pq$. This indicates that the 2-adic complexity of modified Jacobi sequence is large enough to resist the attack of the rational approximation algorithm (RAA) for feedback with carry shift registers (FCSRs).
Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks
Venue: arXiv cs.CR 2017
Year: 2017

Despite the wide use of machine learning in adversarial settings including computer security, recent studies have demonstrated vulnerabilities to evasion attacks---carefully crafted adversarial samples that closely resemble legitimate instances, but cause misclassification. In this paper, we examine the adequacy of the leading approach to generating adversarial samples---the gradient descent approach. In particular (1) we perform extensive experiments on three datasets, MNIST, USPS and Spambase, in order to analyse the effectiveness of the gradient-descent method against non-linear support vector machines, and conclude that carefully reduced kernel smoothness can significantly increase robustness to the attack; (2) we demonstrate that separated inter-class support vectors lead to more secure models, and propose a quantity similar to margin that can efficiently predict potential susceptibility to gradient-descent attacks, before the attack is launched; and (3) we design a new adversarial sample construction algorithm based on optimising the multiplicative ratio of class decision functions.
Transferrable Plausibility Model - A Probabilistic Interpretation of Mathematical Theory of Evidence
Venue: arXiv cs.AI 2017
Year: 2017

This paper suggests a new interpretation of the Dempster-Shafer theory in terms of probabilistic interpretation of plausibility. A new rule of combination of independent evidence is shown and its preservation of interpretation is demonstrated.
MRA - Proof of Concept of a Multilingual Report Annotator Web Application
Venue: arXiv cs.CL 2017
Year: 2017

MRA (Multilingual Report Annotator) is a web application that translates Radiology text and annotates it with RadLex terms. Its goal is to explore the solution of translating non-English Radiology reports as a way to solve the problem of most of the Text Mining tools being developed for English. In this brief paper we explain the language barrier problem and shortly describe the application. MRA can be found at https://github.com/lasigeBioTM/MRA .
Enabling Smart Data: Noise filtering in Big Data classification
Venue: arXiv cs.DB 2017
Year: 2017

In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect labeling of training instances, and is known to be a very disruptive feature of data. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals created to tackle noise, as they have difficulties coping with such a large amount of data. New algorithms need to be proposed to treat the noise in Big Data problems, providing high quality and clean data, also known as Smart Data. In this paper, two Big Data preprocessing approaches to remove noisy examples are proposed: an homogeneous ensemble and an heterogeneous ensemble filter, with special emphasis in their scalability and performance traits. The obtained results show that these proposals enable the practitioner to efficiently obtain a Smart Dataset from any Big Data classification problem.
Geometry of Policy Improvement
Venue: arXiv cs.AI 2017
Year: 2017

We investigate the geometry of optimal memoryless time independent decision making in relation to the amount of information that the acting agent has about the state of the system. We show that the expected long term reward, discounted or per time step, is maximized by policies that randomize among at most $k$ actions whenever at most $k$ world states are consistent with the agent's observation. Moreover, we show that the expected reward per time step can be studied in terms of the expected discounted reward. Our main tool is a geometric version of the policy improvement lemma, which identifies a polyhedral cone of policy changes in which the state value function increases for all states.
Neural Question Generation from Text: A Preliminary Study
Venue: arXiv cs.CL 2017
Year: 2017

Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a preliminary study on neural question generation from text with the SQuAD dataset, and the experiment results show that our method can produce fluent and diverse questions.
Contextual Data Collection for Smart Cities
Venue: arXiv cs.AI 2017
Year: 2017

As part of Smart Cities initiatives, national, regional and local governments all over the globe are under the mandate of being more open regarding how they share their data. Under this mandate, many of these governments are publishing data under the umbrella of open government data, which includes measurement data from city-wide sensor networks. Furthermore, many of these data are published in so-called data portals as documents that may be spreadsheets, comma-separated value (CSV) data files, or plain documents in PDF or Word documents. The sharing of these documents may be a convenient way for the data provider to convey and publish data but it is not the ideal way for data consumers to reuse the data. For example, the problems of reusing the data may range from difficulty opening a document that is provided in any format that is not plain text, to the actual problem of understanding the meaning of each piece of knowledge inside of the document. Our proposal tackles those challenges by identifying metadata that has been regarded to be relevant for measurement data and providing a schema for this metadata. We further leverage the Human-Aware Sensor Network Ontology (HASNetO) to build an architecture for data collected in urban environments. We discuss the use of HASNetO and the supporting infrastructure to manage both data and metadata in support of the City of Fortaleza, a large metropolitan area in Brazil.
Human-Aware Sensor Network Ontology: Semantic Support for Empirical Data Collection
Venue: arXiv cs.AI 2017
Year: 2017

Significant efforts have been made to understand and document knowledge related to scientific measurements. Many of those efforts resulted in one or more high-quality ontologies that describe some aspects of scientific measurements, but not in a comprehensive and coherently integrated manner. For instance, we note that many of these high-quality ontologies are not properly aligned, and more challenging, that they have different and often conflicting concepts and approaches for encoding knowledge about empirical measurements. As a result of this lack of an integrated view, it is often challenging for scientists to determine whether any two scientific measurements were taken in semantically compatible manners, thus making it difficult to decide whether measurements should be analyzed in combination or not. In this paper, we present the Human-Aware Sensor Network Ontology that is a comprehensive alignment and integration of a sensing infrastructure ontology and a provenance ontology. HASNetO has been under development for more than one year, and has been reviewed, shared and used by multiple scientific communities. The ontology has been in use to support the data management of a number of large-scale ecological monitoring activities (observations) and empirical experiments.
Short Labeling Schemes for Topology Recognition in Wireless Tree Networks
Venue: arXiv cs.DC 2017
Year: 2017

We consider the problem of topology recognition in wireless (radio) networks modeled as undirected graphs. Topology recognition is a fundamental task in which every node of the network has to output a map of the underlying graph i.e., an isomorphic copy of it, and situate itself in this map. In wireless networks, nodes communicate in synchronous rounds. In each round a node can either transmit a message to all its neighbors, or stay silent and listen. At the receiving end, a node $v$ hears a message from a neighbor $w$ in a given round, if $v$ listens in this round, and if $w$ is its only neighbor that transmits in this round. Nodes have labels which are (not necessarily different) binary strings. The length of a labeling scheme is the largest length of a label. We concentrate on wireless networks modeled by trees, and we investigate two problems. \begin{itemize} \item What is the shortest labeling scheme that permits topology recognition in all wireless tree networks of diameter $D$ and maximum degree $Δ$? \item What is the fastest topology recognition algorithm working for all wireless tree networks of diameter $D$ and maximum degree $Δ$, using such a short labeling scheme? \end{itemize} We are interested in deterministic topology recognition algorithms. For the first problem, we show that the minimum length of a labeling scheme allowing topology recognition in all trees of maximum degree $Δ\geq 3$ is $Θ(\log\log Δ)$. For such short schemes, used by an algorithm working for the class of trees of diameter $D\geq 4$ and maximum degree $Δ\geq 3$, we show almost matching bounds on the time of topology recognition: an upper bound $O(DΔ)$, and a lower bound $Ω(DΔ^ε)$, for any constant $ε<1$.
An efficient algorithm for compression-based compressed sensing
Venue: arXiv cs.IT 2017
Year: 2017

Modern image and video compression codes employ elaborate structures existing in such signals to encode them into few number of bits. Compressed sensing recovery algorithms on the other hand use such signals' structures to recover them from few linear observations. Despite the steady progress in the field of compressed sensing, structures that are often used for signal recovery are still much simpler than those employed by state-of-the-art compression codes. The main goal of this paper is to bridge this gap through answering the following question: Can one employ a given compression code to build an efficient (polynomial time) compressed sensing recovery algorithm? In response to this question, the compression-based gradient descent (C-GD) algorithm is proposed. C-GD, which is a low-complexity iterative algorithm, is able to employ a generic compression code for compressed sensing and therefore elevates the scope of structures used in compressed sensing to those used by compression codes. The convergence performance of C-GD and its required number of measurements in terms of the rate-distortion performance of the compression code are theoretically analyzed. It is also shown that C-GD is robust to additive white Gaussian noise. Finally, the presented simulation results show that combining C-GD with commercial image compression codes such as JPEG2000 yields state-of-the-art performance in imaging applications.